Download or read book Improving Object Detection and Segmentation by Utilizing Context written by Subarna Tripathi and published by . This book was released on 2018 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Object detection and segmentation are important computer vision problems that have applications in several domains such as autonomous driving, virtual and augmented reality systems, human-computer interaction etc. In this dissertation, we study how to improve object detection and segmentation by utilizing different contexts. Context refers to one of many application scenarios such as (i) video frames for consistent prediction over time, (ii) specific domain knowledge such as human keypoints for person segmentation, and (iii) implementation context aiming for efficiency in embedded systems. Temporal Context of Videos: Video data understanding has drawn considerable interest in recent times as a result of access to huge amount of video data and success in image-based models for visual tasks. However, motion blur, compression artifacts cause apparently consistent video signals to produce high temporal variation on frame-level output for vision tasks such as object detection or semantic segmentation. We study and propose efficient early, and high-level visual processing algorithms by leveraging video content in a streaming fashion. We show how to fuse motion and color to achieve improved streaming hierarchical supervoxels. As a high-level visual task, we propose consistent and efficient video object detection using Convolutional Neural Network (CNN) by clustering video object proposals and propagating object class labels through the videos. Next, we propose an end-to-end framework for learning video object detection through Recurrent Neural Network (RNN) by posing video as a time series. We also present a post-processing framework for improving semantic segmentation in videos. Domain Knowledge Context for Segmentation: Person instance segmentation is a promising research frontier for a range of applications such as human-robot interaction, sports performance analysis, and action recognition. Human keypoints are a well-studied representation of people. We explore how to use keypoint models to improve instance-level person segmentation in constrained and unconstrained environments with or without training. Efficiency Context for Embedded Implementation: To make an object detector system amenable for embedded implementation, we propose a low-complexity fully convolutional neural network. Additionally, we employ 8-bit quantization on the learned weights. As a mobile use case, we choose face detection. The results show that the proposed method achieves comparative accuracy comparing with the state-of-the-art CNN-based object detection methods while reducing the model size by 3x and memory-BW by 3-4x comparing with its strongest baseline.

Download or read book Practical Machine Learning for Computer Vision written by Valliappa Lakshmanan and published by "O'Reilly Media, Inc.". This book was released on 2021-07-21 with total page 481 pages. Available in PDF, EPUB and Kindle. Book excerpt: This practical book shows you how to employ machine learning models to extract information from images. ML engineers and data scientists will learn how to solve a variety of image problems including classification, object detection, autoencoders, image generation, counting, and captioning with proven ML techniques. This book provides a great introduction to end-to-end deep learning: dataset creation, data preprocessing, model design, model training, evaluation, deployment, and interpretability. Google engineers Valliappa Lakshmanan, Martin Görner, and Ryan Gillard show you how to develop accurate and explainable computer vision ML models and put them into large-scale production using robust ML architecture in a flexible and maintainable way. You'll learn how to design, train, evaluate, and predict with models written in TensorFlow or Keras. You'll learn how to: Design ML architecture for computer vision tasks Select a model (such as ResNet, SqueezeNet, or EfficientNet) appropriate to your task Create an end-to-end ML pipeline to train, evaluate, deploy, and explain your model Preprocess images for data augmentation and to support learnability Incorporate explainability and responsible AI best practices Deploy image models as web services or on edge devices Monitor and manage ML models

Download or read book Computer Vision ECCV 2008 written by David Hutchison and published by . This book was released on 2008 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The four-volume set comprising LNCS volumes 5302/5303/5304/5305 constitutes the refereed proceedings of the 10th European Conference on Computer Vision, ECCV 2008, held in Marseille, France, in October 2008. The 243 revised papers presented were carefully reviewed and selected from a total of 871 papers submitted. The four books cover the entire range of current issues in computer vision. The papers are organized in topical sections on recognition, stereo, people and face recognition, object tracking, matching, learning and features, MRFs, segmentation, computational photography and active reconstruction.

Download or read book Improving Deep Learning Based Semantic Segmentation Using Context Information written by Zhengyu Xia and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Computer Vision ECCV 2020 written by Andrea Vedaldi and published by Springer Nature. This book was released on 2020-11-11 with total page 832 pages. Available in PDF, EPUB and Kindle. Book excerpt: The 30-volume set, comprising the LNCS books 12346 until 12375, constitutes the refereed proceedings of the 16th European Conference on Computer Vision, ECCV 2020, which was planned to be held in Glasgow, UK, during August 23-28, 2020. The conference was held virtually due to the COVID-19 pandemic. The 1360 revised papers presented in these proceedings were carefully reviewed and selected from a total of 5025 submissions. The papers deal with topics such as computer vision; machine learning; deep neural networks; reinforcement learning; object recognition; image classification; image processing; object detection; semantic segmentation; human pose estimation; 3d reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; object recognition; motion estimation.

Download or read book Improving Image Segmentation by Learning Region Affinities written by and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We utilize the context information of other regions in hierarchical image segmentation to learn new regions affinities. It is well known that a single choice of quantization of an image space is highly unlikely to be a common optimal quantization level for all categories. Each level of quantization has its own benefits. Therefore, we utilize the hierarchical information among different quantizations as well as spatial proximity of their regions. The proposed affinity learning takes into account higher order relations among image regions, both local and long range relations, making it robust to instabilities and errors of the original, pairwise region affinities. Once the learnt affinities are obtained, we use a standard image segmentation algorithm to get the final segmentation. Moreover, the learnt affinities can be naturally unutilized in interactive segmentation. Experimental results on Berkeley Segmentation Dataset and MSRC Object Recognition Dataset are comparable and in some aspects better than the state-of-art methods.

Download or read book Moving Objects Detection Using Machine Learning written by Navneet Ghedia and published by Springer Nature. This book was released on 2022-01-01 with total page 91 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book shows how machine learning can detect moving objects in a digital video stream. The authors present different background subtraction approaches, foreground segmentation, and object tracking approaches to accomplish this. They also propose an algorithm that considers a multimodal background subtraction approach that can handle a dynamic background and different constraints. The authors show how the proposed algorithm is able to detect and track 2D & 3D objects in monocular sequences for both indoor and outdoor surveillance environments and at the same time, also able to work satisfactorily in a dynamic background and with challenging constraints. In addition, the shows how the proposed algorithm makes use of parameter optimization and adaptive threshold techniques as intrinsic improvements of the Gaussian Mixture Model. The presented system in the book is also able to handle partial occlusion during object detection and tracking. All the presented work and evaluations were carried out in offline processing with the computation done by a single laptop computer with MATLAB serving as software environment.

Download or read book Computer Vision ECCV 2014 written by David Fleet and published by Springer. This book was released on 2014-08-14 with total page 878 pages. Available in PDF, EPUB and Kindle. Book excerpt: The seven-volume set comprising LNCS volumes 8689-8695 constitutes the refereed proceedings of the 13th European Conference on Computer Vision, ECCV 2014, held in Zurich, Switzerland, in September 2014. The 363 revised papers presented were carefully reviewed and selected from 1444 submissions. The papers are organized in topical sections on tracking and activity recognition; recognition; learning and inference; structure from motion and feature matching; computational photography and low-level vision; vision; segmentation and saliency; context and 3D scenes; motion and 3D scene analysis; and poster sessions.

Download or read book Moving Object Detection Using Background Subtraction written by Soharab Hossain Shaikh and published by Springer. This book was released on 2014-06-20 with total page 74 pages. Available in PDF, EPUB and Kindle. Book excerpt: This Springer Brief presents a comprehensive survey of the existing methodologies of background subtraction methods. It presents a framework for quantitative performance evaluation of different approaches and summarizes the public databases available for research purposes. This well-known methodology has applications in moving object detection from video captured with a stationery camera, separating foreground and background objects and object classification and recognition. The authors identify common challenges faced by researchers including gradual or sudden illumination change, dynamic backgrounds and shadow and ghost regions. This brief concludes with predictions on the future scope of the methods. Clear and concise, this brief equips readers to determine the most effective background subtraction method for a particular project. It is a useful resource for professionals and researchers working in this field.

Download or read book Architecture of Computing Systems written by Martin Schulz and published by Springer Nature. This book was released on 2022-12-13 with total page 293 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the proceedings of the 35th International Conference on Architecture of Computing Systems, ARCS 2022, held virtually in July 2022. The 18 full papers in this volume were carefully reviewed and selected from 35 submissions. ARCS provides a platform covering newly emerging and cross-cutting topics, such as autonomous and ubiquitous systems, reconfigurable computing and acceleration, neural networks and artificial intelligence. The selected papers cover a variety of topics from the ARCS core domains, including energy efficiency, applied machine learning, hardware and software system security, reliable and fault-tolerant systems and organic computing.

Download or read book Improving Object Recognition Performance Through Semantic Context Extraction written by Brigid Smith and published by . This book was released on 2015 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine vision is a computationally expensive problem with an exceptionally largenumber of real-world applications. With the rise of the Internet of Things and the presence ofwearables in day to day settings, there is an additional focus on power constraints and thelimitations of fixed hardware. In a vision pipeline, the accuracy of the object classification stagewill likely affect the usefulness of the pipeline as a whole. However, we find that it is difficult tocreate a system with the ability to recognize a large number of objects both quickly andaccurately because the number of classifiers needed grows with the number of objects. Weobserve that real world images and the objects in them tend to be sensible and exposerelationships between objects and scenes that are used by humans intuitively. This high-levelcontext could potentially be used to inform and improve object classification by allowing us tomake reasonable, probabilistic guesses about objects that might occur based on other informationthat we have about the image. This guesswork will lower the number of classifiers that need tobe run, which will also address power and timing concerns. In this paper, we explore themeaning of context, design a framework to store it in a way accessible to a computer, and thenevaluate the efficacy of context-based filtering.

Download or read book Smart Sensing and Context written by Daniel Roggen and published by Springer Science & Business Media. This book was released on 2008-10-15 with total page 257 pages. Available in PDF, EPUB and Kindle. Book excerpt: This year marks the third edition of EuroSSC. It builds on the success of the past editions, held in Enschede, The Netherlands in 2006, and in Kendal, UK in 2007. On behalf of the Organizing Committee, we would like to welcome you to EuroSSC 2008, in Zurich, Switerland. This volume contains the invited papers and technical peer-reviewed papers selected for presentation at the conference. At EuroSSC we aim to explore technologies, algorithms, architectures, p- tocols, and user aspects underlying context-aware smart surroundings, coop- ating intelligent objects, and their applications. Since its inception, EuroSSC has taken a complementary technology-driven and user-driven view to discuss these aspects. It is one of the particularities of EuroSSC, and the 2008 edition made no exception. In addition we emphasized aspects related to quality of c- text and context-aware feedback by actuator systems. This re?ects the growing importance that context processing in uncertain environments and sensor and actuator networks take in ambient intelligence environments. We received 70 paper submissions. They originate from 30 countries of - rope, the Middle East and Africa (66%), Asia (22%), North America (9%), and South America (3%). These numbers re?ect the European origins of EuroSSC, but also show that EuroSSC is a recognized and attractive platform for parti- pants from all regions of the world.

Download or read book Moving Object Detection Using Background Subtraction Algorithms written by Priyank Shah and published by GRIN Verlag. This book was released on 2014-06-16 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Master's Thesis from the year 2014 in the subject Computer Science - Theory, grade: 9.2, , language: English, abstract: In this thesis we present an operational computer video system for moving object detection and tracking . The system captures monocular frames of background as well as moving object and to detect tracking and identifies those moving objects. An approach to statistically modeling of moving object developed using Background Subtraction Algorithms. There are many methods proposed for Background Subtraction algorithm in past years. Background subtraction algorithm is widely used for real time moving object detection in video surveillance system. In this paper we have studied and implemented different types of methods used for segmentation in Background subtraction algorithm with static camera. This paper gives good understanding about procedure to obtain foreground using existing common methods of Background Subtraction, their complexity, utility and also provide basics which will useful to improve performance in the future . First, we have explained the basic steps and procedure used in vision based moving object detection. Then, we have debriefed the common methods of background subtraction like Simple method, statistical methods like Mean and Median filter, Frame Differencing and W4 System method , Running Gaussian Average and Gaussian Mixture Model and last is Eigenbackground Model. After that we have implemented all the above techniques on MATLAB software and show some experimental results for the same and compare them in terms of speed and complexity criteria. Also we have improved one of the GMM algorithm by combining it with optical flow method, which is also good method to detect moving elements.

Download or read book A New Algorithm for Improving Basic Model Based Foreground Detection Using Neutrosophic Similarity Score written by Keli Hu and published by Infinite Study. This book was released on with total page 10 pages. Available in PDF, EPUB and Kindle. Book excerpt: Foreground detection is a task for detecting the moving objects in the scene like in video surveillance. Several basic background models are often used due to their high efficiency. However, their results are not good when there exists noisy information generated by the bad weather, camera jitter, etc. Neutrosophic set (NS) is as a new branch of philosophy dealing with the origin, nature and scope of neutralities. It has an inherent ability to handle the indeterminant information like the noise included in images and video sequences.

Download or read book Context driven Object Detection and Segmentation with Auxiliary Information written by Tao Wang and published by . This book was released on 2016 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One fundamental problem in computer vision and robotics is to localize objects of interest in an image. The task can either be formulated as an object detection problem if the objects are described by a set of pose parameters, or an object segmentation one if we recover object boundary precisely. A key issue in object detection and segmentation concerns exploiting the spatial context, as local evidence is often insufficient to determine object pose in the presence of heavy occlusions or large object appearance variations. This thesis addresses the object detection and segmentation problem in such adverse conditions with auxiliary depth data provided by RGBD cameras. We focus on four main issues in context-aware object detection and segmentation: 1) what are the effective context representations? 2) how can we work with limited and imperfect depth data? 3) how to design depth-aware features and integrate depth cues into conventional visual inference tasks? 4) how to make use of unlabeled data to relax the labeling requirements for training data? We discuss three object detection and segmentation scenarios based on varying amounts of available auxiliary information. In the first case, depth data are available for model training but not available for testing. We propose a structured Hough voting method for detecting objects with heavy occlusion in indoor environments, in which we extend the Hough hypothesis space to include both the object's location, and its visibility pattern. We design a new score function that accumulates votes for object detection and occlusion prediction. In addition, we explore the correlation between objects and their environment, building a depth-encoded object-context model based on RGBD data. In the second case, we address the problem of localizing glass objects with noisy and incomplete depth data. Our method integrates the intensity and depth information from a single view point, and builds a Markov Random Field that predicts glass boundary and region jointly. In addition, we propose a nonparametric, data-driven label transfer scheme for local glass boundary estimation. A weighted voting scheme based on a joint feature manifold is adopted to integrate depth and appearance cues, and we learn a distance metric on the depth-encoded feature manifold. In the third case, we make use of unlabeled data to relax the annotation requirements for object detection and segmentation, and propose a novel data-dependent margin distribution learning criterion for boosting, which utilizes the intrinsic geometric structure of datasets. One key aspect of this method is that it can seamlessly incorporate unlabeled data by including a graph Laplacian regularizer. We demonstrate the performance of our models and compare with baseline methods on several real-world object detection and segmentation tasks, including indoor object detection, glass object segmentation and foreground segmentation in video.

Download or read book Learning with Limited Annotated Data for Visual Understanding written by Mikita Dvornik and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The ability of deep-learning methods to excel in computer vision highly depends on the amount of annotated data available for training. For some tasks, annotation may be too costly and labor intensive, thus becoming the main obstacle to better accuracy. Algorithms that learn from data automatically, without human supervision, perform substantially worse than their fully-supervised counterparts. Thus, there is a strong motivation to work on effective methods for learning with limited annotations. This thesis proposes to exploit prior knowledge about the task and develops more effective solutions for scene understanding and few-shot image classification.Main challenges of scene understanding include object detection, semantic and instance segmentation. Similarly, all these tasks aim at recognizing and localizing objects, at region- or more precise pixel-level, which makes the annotation process difficult. The first contribution of this manuscript is a Convolutional Neural Network (CNN) that performs both object detection and semantic segmentation. We design a specialized network architecture, that is trained to solve both problems in one forward pass, and operates in real-time. Thanks to the multi-task training procedure, both tasks benefit from each other in terms of accuracy, with no extra labeled data.The second contribution introduces a new technique for data augmentation, i.e., artificially increasing the amount of training data. It aims at creating new scenes by copy-pasting objects from one image to another, within a given dataset. Placing an object in a right context was found to be crucial in order to improve scene understanding performance. We propose to model visual context explicitly using a CNN that discovers correlations between object categories and their typical neighborhood, and then proposes realistic locations for augmentation. Overall, pasting objects in ``right'' locations allows to improve object detection and segmentation performance, with higher gains in limited annotation scenarios.For some problems, the data is extremely scarce, and an algorithm has to learn new concepts from a handful of examples. Few-shot classification consists of learning a predictive model that is able to effectively adapt to a new class, given only a few annotated samples. While most current methods concentrate on the adaptation mechanism, few works have tackled the problem of scarce training data explicitly. In our third contribution, we show that by addressing the fundamental high-variance issue of few-shot learning classifiers, it is possible to significantly outperform more sophisticated existing techniques. Our approach consists of designing an ensemble of deep networks to leverage the variance of the classifiers, and introducing new strategies to encourage the networks to cooperate, while encouraging prediction diversity. By matching different networks outputs on similar input images, we improve model accuracy and robustness, comparing to classical ensemble training. Moreover, a single network obtained by distillation shows similar to the full ensemble performance and yields state-of-the-art results with no computational overhead at test time.

Download or read book ECAI 2023 written by K. Gal and published by IOS Press. This book was released on 2023-10-18 with total page 3328 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial intelligence, or AI, now affects the day-to-day life of almost everyone on the planet, and continues to be a perennial hot topic in the news. This book presents the proceedings of ECAI 2023, the 26th European Conference on Artificial Intelligence, and of PAIS 2023, the 12th Conference on Prestigious Applications of Intelligent Systems, held from 30 September to 4 October 2023 and on 3 October 2023 respectively in Kraków, Poland. Since 1974, ECAI has been the premier venue for presenting AI research in Europe, and this annual conference has become the place for researchers and practitioners of AI to discuss the latest trends and challenges in all subfields of AI, and to demonstrate innovative applications and uses of advanced AI technology. ECAI 2023 received 1896 submissions – a record number – of which 1691 were retained for review, ultimately resulting in an acceptance rate of 23%. The 390 papers included here, cover topics including machine learning, natural language processing, multi agent systems, and vision and knowledge representation and reasoning. PAIS 2023 received 17 submissions, of which 10 were accepted after a rigorous review process. Those 10 papers cover topics ranging from fostering better working environments, behavior modeling and citizen science to large language models and neuro-symbolic applications, and are also included here. Presenting a comprehensive overview of current research and developments in AI, the book will be of interest to all those working in the field.